CN112994740B - Frequency hopping signal parameter estimation method, apparatus, electronic device and readable storage medium - Google Patents

Abstract

Embodiments of the present application provide a frequency hopping signal parameter estimation method, apparatus, electronic device, and readable storage medium, and relate to the technical field of communications. The analysis results are obtained by acquiring the data to be processed, and performing power time analysis processing and frequency time analysis processing on the data to be processed. Whether the data to be processed is a frequency hopping signal is determined according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, the parameters of the data to be processed are estimated according to the analysis result to obtain an estimation result. In this way, the data to be processed is analyzed and processed in combination with the power time analysis process and the frequency time analysis process, which improves the accuracy of frequency hopping parameter estimation in complex engineering applications.

Description

Frequency hopping signal parameter estimation method, apparatus, electronic device and readable storage medium

technical field

The present application relates to the field of communication technologies, and in particular, to a frequency hopping signal parameter estimation method, apparatus, electronic device, and readable storage medium.

Background technique

In recent years, with the rapid development of modern information technology, frequency hopping communication technology has been widely used in military and civilian communication fields due to its good anti-interference, safety and reliability, and low interception capability. On the one hand, as the main technical means of military communication, the application of frequency hopping communication technology in short-wave and ultra-short-wave radio stations has greatly improved the combat effectiveness of the troops; on the other hand, its application in civil mobile communication, modern radar and sonar and other electronic systems It has accelerated the development of modern communication technology. Therefore, the relevant research on frequency hopping communication technology has a profound impact on military exchanges and scientific and technological progress.

At present, the frequencies are often clustered, and then the hopping time and the frequency hopping time are calculated according to the hopping frames. Although the estimation method based on frequency hopping can effectively estimate the frequency, the estimation of the frequency hopping moment can only roughly locate the number of frames, so there is a large error, which has a great impact on the estimation of the switching time of the frequency hopping signal. .

At present, filters are often used to extract each frequency component of the frequency hopping signal, and after linear superposition, a time-frequency distribution with high time-frequency resolution is obtained, thereby obtaining the dwell time. However, it is difficult to select the filter in this method, and the time-frequency resolution is greatly affected by the filter sideband.

How to improve the accuracy of frequency hopping parameter estimation in complex engineering applications is a problem worth studying at present.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present application provide a frequency hopping signal parameter estimation method, apparatus, electronic device, and readable storage medium to solve the above problems.

In a first aspect, the present application provides a method for estimating a frequency hopping signal parameter, the method comprising:

Get data to be processed;

Performing power time analysis processing and frequency time analysis processing on the data to be processed to obtain analysis results;

Whether the data to be processed is a frequency hopping signal is determined according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, parameters of the data to be processed are estimated according to the analysis result to obtain an estimation result.

In an optional embodiment, the frequency-time analysis processing includes short-time Fourier transform processing, spectrogram processing, and frequency clustering processing, and the data to be processed is subjected to power-time analysis processing and frequency-time analysis processing , the steps to obtain the analysis result include:

Perform power-time analysis processing on the data to be processed to obtain a power-time analysis result;

performing short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;

Perform spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;

performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;

The power time analysis result and the window frequency data of the data to be processed are used as the analysis result.

In an optional implementation manner, the data to be processed includes an in-phase quadrature signal and a sampling frequency, the spectrogram processing result includes spectral data, and the parameters of the data to be processed are estimated according to the analysis result, The steps to arrive at an estimate include:

According to the in-phase quadrature signal and the sampling frequency included in the data to be processed, set a time multiple threshold and a power judgment threshold;

Calculate the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold;

performing positive peak positioning processing on the spectral data included in the spectrogram processing result to obtain the dwell time of the data to be processed;

The switching time and the dwell time are used as estimation results.

In an optional implementation manner, the step of calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold includes:

obtaining a switching time period in which the power in the power time analysis result is less than the power judgment threshold;

Obtaining a power conversion critical point according to the time multiple threshold, and calculating a start point and an end point of each switching time period according to the power conversion critical point;

Calculate the initial switching time at the start point and the end point of each of the switching time periods, and calculate the average value of all the initial switching times;

The average value of the initial switching time is taken as the switching time of the data to be processed.

In an optional embodiment, the positive peak location processing includes first-order derivative processing, smoothing processing, and zero-crossing point detection, and the positive peak location processing is performed on the spectral data included in the spectrogram processing result to obtain the to-be-to-be The steps to handle the dwell time of data include:

calculating the first-order derivative of the spectral data to obtain a derivation result, wherein the derivation result represents all maximum points of the spectral data;

smoothing the derivation result to obtain a smoothed derivation result;

Perform zero-crossing detection on the derivation result after smoothing to obtain the signal spectrum peak;

Screening to obtain a positive spectral peak in the signal spectral peaks and a frequency corresponding to the positive spectral peak;

The dwell time of the data to be processed is calculated according to the frequency.

In an optional implementation manner, the step of calculating the dwell time of the data to be processed according to the frequency includes:

Calculate the frequency step size of the positive spectral peak according to the frequency;

Calculate the frequency interval of each of the positive spectral peaks according to the frequency step size, and use the smallest frequency interval as the frequency step size of the data to be processed;

Calculate the frequency difference of all adjacent frequencies, and filter to obtain a frequency set whose frequency difference is greater than half of the frequency step size;

The dwell time of the data to be processed is obtained according to the frequency set.

In an optional embodiment, the method further includes:

According to the analysis result, determine whether the data to be processed includes multiple complete switching time periods and includes multiple complete dwell time periods;

If it is determined that the data to be processed includes multiple complete switching time periods and multiple complete dwell time periods, the dwell time corresponding to the smallest dwell time period is used as the dwell time of the to-be-processed data , and calculate the average value of the switching time corresponding to a plurality of complete switching time periods, and use the average value as the switching time of the data to be processed.

In a second aspect, the present application provides a frequency hopping signal parameter estimation device, the device comprising:

The acquisition module is used to acquire the data to be processed;

an analysis and processing module, configured to perform power-time analysis and processing and frequency-time analysis and processing on the data to be processed to obtain analysis results;

an estimation module, configured to judge whether the data to be processed is a frequency hopping signal according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, then estimate the parameters of the data to be processed according to the analysis result , to get the estimated result.

In a third aspect, the present application provides an electronic device, the electronic device includes a processor, a memory, and a bus, the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processing A bus communicates between the processor and the memory, and the processor executes the machine-readable instructions to perform the steps of the method for estimating a frequency hopping signal parameter according to any one of the foregoing embodiments.

In a fourth aspect, the present application provides a readable storage medium, where the readable storage medium stores a computer program, and when the computer program is executed, implements the steps of the frequency hopping signal parameter estimation method described in any one of the foregoing embodiments.

The embodiments of the present application provide a method, apparatus, electronic device and readable storage medium for estimating parameters of a frequency hopping signal. The analysis results are obtained by acquiring data to be processed, and performing power time analysis processing and frequency time analysis processing on the data to be processed. Whether the data to be processed is a frequency hopping signal is determined according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, the parameters of the data to be processed are estimated according to the analysis result to obtain an estimation result. In this way, the data to be processed is analyzed and processed in combination with the power time analysis process and the frequency time analysis process, which improves the accuracy of frequency hopping parameter estimation in complex engineering applications.

In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, some examples are given below for detailed description in conjunction with the accompanying drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following drawings will briefly introduce the drawings that need to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a structural block diagram of an electronic device provided by an embodiment of the present application.

FIG. 2 is a schematic flowchart of a method for estimating parameters of a frequency hopping signal provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of locating a power time critical point according to an embodiment of the present application.

FIG. 4 is a schematic diagram of positive peak positioning provided in an embodiment of the present application.

FIG. 5 is a schematic diagram of comparison of switching time estimation errors provided by an embodiment of the present application.

FIG. 6 is a schematic diagram of a comparison of residence time estimation errors provided by an embodiment of the present application.

FIG. 7 is a functional block diagram of an apparatus for estimating parameters of a frequency hopping signal provided by an embodiment of the present application.

Icons: 100-electronic equipment; 110-memory; 120-processor; 130-frequency hopping signal parameter estimation device; 131-acquisition module; 132-analysis processing module; 133-estimation module; 140-communication unit.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of the present application, but not all of the embodiments. The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

Thus, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

In addition, where the terms "first", "second" and the like appear, they are only used to differentiate the description, and should not be construed as indicating or implying relative importance.

It should be noted that the features in the embodiments of the present application may be combined with each other under the condition of no conflict.

As described in the background art, in recent years, with the rapid development of modern information technology, frequency hopping communication technology has been widely used in military and civilian fields due to its good anti-interference, safety and reliability, and low interception capabilities. communication field. On the one hand, as the main technical means of military communication, the application of frequency hopping communication technology in short-wave and ultra-short-wave radio stations has greatly improved the combat effectiveness of the troops; on the other hand, its application in civil mobile communication, modern radar and sonar and other electronic systems It has accelerated the development of modern communication technology. Therefore, the relevant research on frequency hopping communication technology has a profound impact on military exchanges and scientific and technological progress.

For the research on the parameter estimation method of the frequency hopping signal, the research methods of domestic scholars are mainly based on the time-frequency analysis. For example, Feng Tao, Yuan Chaowei. A new method for time-frequency analysis of frequency hopping signals [J]. Journal of Beijing University of Posts and Telecommunications, 2010, 033(003): 10-14. A time-frequency analysis method based on signal decomposition is proposed. The method uses a filter to extract each frequency component of the frequency hopping signal, and obtains a time-frequency distribution with high time-frequency resolution after linear superposition, thereby obtaining the dwell time. However, it is difficult to select the filter in this method, and the time-frequency resolution is greatly affected by the filter sideband. Literature Hu Yanglin. Research and implementation of blind detection and parameter blind estimation algorithm of frequency hopping signal [D]. University of Electronic Science and Technology of China, 2016. The estimation method based on frequency hopping is adopted. The main idea of this method is to cluster the frequencies first, and then calculate the hopping time and frequency hopping time according to the hopping frames. Although the estimation method based on frequency hopping can effectively estimate the frequency, the estimation of the frequency hopping moment can only roughly locate the number of frames, so there is a large error, which has a great impact on the estimation of the switching time of the frequency hopping signal. . Moreover, most of the current frequency hopping signal parameter estimation methods are only theoretically analyzed in the Gaussian white noise environment, and do not consider the special case of power change when hopping occurs. Therefore, they cannot satisfy the effective estimation of frequency hopping parameters in complex engineering applications. need.

How to improve the accuracy of frequency hopping parameter estimation in complex engineering applications is a problem worth studying at present.

In view of this, embodiments of the present application provide a method, apparatus, electronic device, and readable storage medium for estimating parameters of a frequency hopping signal, which combine power and time analysis processing (Power and Time, PVT) and frequency and time analysis processing (Frequency and Time) , FVT) technology analyzes and processes the data to be processed, and at the same time comprehensively judges whether the frequency hopping signal (data to be processed) hops from the power change and frequency change of the hopping point, which enhances the judgment accuracy of the hopping moment of the frequency hopping signal. , thereby improving the accuracy of frequency hopping parameter estimation.

The defects of the above solutions in the prior art are the results obtained by the applicant after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present application below for the above problems , should be the contributions made by the applicant to this application during the application process.

Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the case of no conflict, the following embodiments and the keys in the embodiments can be combined with each other.

Please refer to FIG. 1 , which is a structural block diagram of an electronic device 100 according to an embodiment of the present application. The device may include a processor 120, a memory 110, a frequency hopping signal parameter estimation device 130, and a communication unit 140. The memory 110 stores machine-readable instructions executable by the processor 120. When the electronic device 100 is running, the processor 120 and the memory 110 Through bus communication therebetween, the processor 120 executes machine-readable instructions and executes the method for estimating the parameters of the frequency hopping signal.

The elements of the memory 110 , the processor 120 and the communication unit 140 are directly or indirectly electrically connected to each other to realize signal transmission or interaction.

For example, these elements may be electrically connected to each other through one or more communication buses or signal lines. The frequency hopping signal parameter estimation apparatus 130 includes at least one software function module that can be stored in the memory 110 in the form of software or firmware. The processor 120 is configured to execute executable modules stored in the memory 110 , such as software function modules or computer programs included in the frequency hopping signal parameter estimation apparatus 130 .

The memory 110 may be, but not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable memory In addition to read-only memory (Erasable Programmable Read-Only Memory, EPROM), Electrical Erasable Programmable Read-Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM) and so on.

The processor 120 may be an integrated circuit chip with signal processing capability. The foregoing processor 120 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), and the like.

It may also be a digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In this embodiment of the present application, the memory 110 is used to store the program, and the processor 120 is used to execute the program after receiving the execution instruction. The method for process definition disclosed in any implementation manner of this embodiment of the present application may be applied to the processor 120 or implemented by the processor 120 .

The communication unit 140 is used to establish a communication connection between the electronic device 100 and other electronic devices through the network, and to send and receive data through the network.

In some embodiments, the network may be any type of wired or wireless network, or a combination thereof. By way of example only, networks may include wired networks, wireless networks, fiber optic networks, telecommunications networks, intranets, the Internet, Local Area Network (LAN), Wide Area Network (WAN), Wireless Local Area Networks , WLAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), Public Switched Telephone Network (PSTN), Bluetooth network, ZigBee network, or Near Field Communication (Near Field Communication) Communication, NFC) network, etc., or any combination thereof.

In this embodiment of the present application, the electronic device 100 may be, but is not limited to, a device with processing functions, such as a smartphone, a personal computer, and a tablet computer.

It can be understood that the structure shown in FIG. 1 is for illustration only. The electronic device 100 may also have more or fewer components than shown in FIG. 1 , or have a different configuration than that shown in FIG. 1 . Each component shown in FIG. 1 may be implemented by hardware, software or a combination thereof.

As an effective anti-jamming communication technology, one of the main tasks of frequency hopping communication technology is to estimate the parameters of the frequency hopping signal, including the frequency hopping rate, the frequency hopping bandwidth, the switching time, and the number of frequency hopping frequencies. Among them, the frequency hopping rate and switching time are the key technical indicators to characterize the parameter estimation of the frequency hopping signal, and the frequency hopping rate is equal to the inverse of the dwell time. Therefore, this application mainly studies the dwell time and switching time of the frequency hopping signal.

The steps of the frequency hopping signal parameter estimation method, apparatus, electronic device, and readable storage medium provided by the embodiments of the present application are described in detail below based on the structural diagram of the electronic device 100 shown in FIG. 1 .

Please refer to FIG. 2 , which is a schematic flowchart of a method for estimating parameters of a frequency hopping signal provided by an embodiment of the present application.

Step S1, acquiring data to be processed.

Step S2: Perform power time analysis processing and frequency time analysis processing on the data to be processed to obtain analysis results.

Step S3, according to the analysis result, determine whether the data to be processed is a frequency hopping signal, and if it is determined that the data to be processed is a frequency hopping signal, estimate the parameters of the data to be processed according to the analysis result to obtain an estimation result.

Among them, the data to be processed may or may not be a frequency hopping signal. Therefore, it is necessary to determine in advance whether the data to be processed is a frequency hopping signal. When the data to be processed is a frequency hopping signal, the analysis results are used to parameterize the data to be processed. It is estimated that the estimation results in this embodiment of the present application are the switching time and the dwell time of the data to be processed.

The frequency hopping signal parameter estimation method provided by the embodiment of the present application combines power time analysis processing and frequency time analysis processing, and at the same time comprehensively judges whether the signal hopping occurs from the power change and frequency change of the hopping point, thereby enhancing the frequency hopping signal hopping. The judgment accuracy of variable time can avoid the problem of multiplying the dwell time caused by the frequency hopping signal when the power of the frequency hopping signal does not change at the hopping point when only the power time analysis is used; , the switching time estimation deviation is too large, thereby improving the accuracy of the parameter estimation of the frequency hopping signal.

In an optional embodiment, the frequency-time analysis processing includes Short Time Fourier Transform (STFT) processing, Spectrum Picture (SP) processing, and frequency clustering processing, the steps shown in FIG. 2 . S2, perform power time analysis processing and frequency time analysis processing on the data to be processed, and obtain the analysis results by the following methods:

Perform power-time analysis processing on the data to be processed to obtain a power-time analysis result. The data to be processed is processed by short-time Fourier transform to obtain the time-frequency characteristics of the data to be processed. Perform spectrogram processing on the time-frequency features of the data to be processed to obtain the spectrogram processing result. Perform frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed. The power time analysis result and the window frequency data of the data to be processed are used as the analysis result.

The power-time analysis refers to a power-time analysis method used to describe the combined characteristics of signal power and time. The power time analysis can display the change information of the signal power over time when the power of the frequency hopping signal is unknown in advance.

为待处理数据，可利用如下功率-时间联合函数

对待处理数据（待处理数据）进行功率时间分析处理：Optionally, set

For the data to be processed, the following power-time joint function can be used

Perform power time analysis processing on the data to be processed (data to be processed):

也为功率时间分析结果。According to the analysis result of the power time analysis processing of the data to be processed, the change of the power at the transition point corresponding to the data to be processed can be displayed, so as to realize the preliminary judgment of the transition time. It is understandable that in the formula

Also analyze the results for power time.

的短时傅里叶变换定义为：Optionally, in the SFTF process, the non-stationary signal in the time domain is first subjected to windowing and segmentation processing, that is, the entire time domain process is decomposed into multiple processes by moving the window on the time axis, and each segment is divided into multiple processes. The signal within the segment is approximately a stationary signal. Then, a Fast Fourier Transform (FFT) is performed on each segmented signal to obtain the spectral information of the signal in each segment. Finally, the time-frequency characteristics of the signal are obtained by combining each piece of spectrum information in time sequence. According to the STFT principle, the data to be processed

The short-time Fourier transform of is defined as:

为时间，

为频率，

为窗函数。短时傅里叶变换算法不含交叉干扰项，且利用快速傅里叶变换可以达到较高的运算速度，算法复杂度较低。但是，STFT方法的缺点是时间分辨率和频率分辨率相互制约，无法达到最佳。因此，对短时傅里叶变换后的时频数据进行模平方处理，得到谱图以提高时频分辨率，即：in,

for time,

is the frequency,

is the window function. The short-time Fourier transform algorithm does not contain cross-interference terms, and the fast Fourier transform can be used to achieve high operation speed and low algorithm complexity. However, the disadvantage of the STFT method is that the time resolution and frequency resolution are mutually restricted and cannot be optimal. Therefore, the time-frequency data after short-time Fourier transform is modulo-squared to obtain a spectrogram to improve the time-frequency resolution, namely:

，分段个数为

，则总的信号频率矩阵F可以表示为：After passing through the STFT process and the SP process, the spectral information of each segment is analyzed. Let the window length be

, the number of segments is

, then the total signal frequency matrix F can be expressed as:

段窗频率

，再通过比较相邻窗频率值大小可以实现待处理数据跳变时刻的粗估计。To cluster the frequencies in each window, the main operation process is to first compare the amplitudes of adjacent frequency points within each window length, and then select the frequency point corresponding to the largest amplitude as the frequency of the window. obtained by frequency clustering

segment window frequency

, and then by comparing the frequency values of adjacent windows, a rough estimation of the transition moment of the data to be processed can be achieved.

In an optional embodiment, the data to be processed includes an in-phase quadrature signal and a sampling frequency, and the spectrogram processing result includes spectral data. According to the analysis result, parameters of the data to be processed are estimated, and the steps of obtaining the estimation result include:

According to the in-phase quadrature signal and the sampling frequency included in the data to be processed, a time multiple threshold and a power judgment threshold are set. The switching time of the data to be processed is calculated according to the power time analysis result, the power judgment threshold and the time multiple threshold. The positive peak positioning process is performed on the spectral data included in the spectrogram processing result to obtain the dwell time of the data to be processed. The switching time and dwell time are used as estimation results.

The set power judgment threshold may be half of the power of the data to be processed. The time multiple threshold can be a multiple of adjacent time intervals. The in-phase quadrature signal is the IQ signal, I is in-phase, and Q is quadrature, which is 90 degrees out of phase with I. Based on the IQ signal, the signal can be easily represented by a complex signal method; the sampling rate of each branch can be reduced (because the sampling rate after amplitude detection will be twice it), reducing the requirements for ADC; at the same time, it can also The phase information of the original signal is preserved.

As an optional implementation manner, the switching time of the data to be processed can be calculated by the following method:

Obtain the switching time period in which the power is less than the power judgment threshold in the power time analysis result. The power conversion critical point is obtained according to the time multiple threshold, and the start point and end point of each switching period are calculated according to the power conversion critical point. Calculate the initial switching times at the start and end points of each switching period, and calculate the average of all initial switching times. The average value of the initial switching time is taken as the switching time of the data to be processed.

筛选出所有切换时间段，即：For example, based on the power judgment threshold

Filter out all switching time periods, namely:

定位到所有临界点

，如图3所示，图3为本申请实施例提供的一种功率时间临界点定位示意图。如图中放大区域所示出的，星号至星号之间为一个切换时间段，星号则表征跳变的临界点，临界点可以表示为：Then, take the difference diff (t _switchpart ) between the adjacent time points in all switching time periods, and according to the time multiple threshold

Locate all critical points

3, FIG. 3 is a schematic diagram of locating a power time critical point according to an embodiment of the present application. As shown in the enlarged area in the figure, the period between the asterisk and the asterisk is a switching time period, and the asterisk represents the critical point of the transition. The critical point can be expressed as:

) t _{critical point} = t ( fun ( diff ( t _switchpar )) ＞

)

可以得到每个切换段的起始点

和结束点

，从而计算出分析时长内待处理数据发生跳变时的所有切换时间，即：According to the critical point

The starting point of each switching segment can be obtained

and end point

, so as to calculate all the switching times when the data to be processed jumps within the analysis duration, namely:

。然后，取分析时长内待处理数据跳变的多个切换时间均值作为待处理数据的切换时间，即：in,

. Then, take the average value of multiple switching times of the data to be processed within the analysis duration as the switching time of the data to be processed, that is:

As an optional implementation manner, the residence time of the data to be processed can be calculated by the following method:

Calculate the first-order derivative of the spectral data to obtain a derivation result, wherein the derivation result represents all the maximum value points of the spectral data. The derivation result is smoothed to obtain the smoothed derivation result. Perform zero-crossing detection on the derivation result after smoothing to obtain signal spectral peaks. The positive spectral peaks in the signal spectral peaks and the frequencies corresponding to the positive spectral peaks are obtained by screening. Calculates the dwell time of data to be processed based on frequency.

The positive peak positioning mechanism refers to a method of calculating the step size of the frequency hopping of the data to be processed by locating the actual peak position of the signal spectrum, which can enhance the validity of the parameter estimation of the data to be processed. Positive peak positioning processing includes first derivative processing, smoothing processing and zero-crossing detection.

In the first-order derivative processing, the first-order derivative process can be regarded as finding the slope of the signal at each point, obtaining the spectral data included in the spectrogram data, and using the center difference first-order derivative to process the spectral data (x, y), Among them, x is the frequency vector corresponding to the data, and y is the magnitude vector. The simple calculation method is:

Considering the influence of random fluctuations, the idea of central difference is used to calculate the average slope between three adjacent points of the signal, namely:

，其包含有效成分

和随机性噪声干扰等导致的随机误差成分

，即：Usually, in the absence of noise, the first-order derivative of the signal spectrum has a downward zero-crossing characteristic at the peak point, but in the actual environment, there will be noise and interference, resulting in many maxima in the signal spectrum. This results in redundant downward zero crossings in the first derivative of the signal spectrum. Therefore, in this technical solution, a pseudo-Gaussian smoothing operation is used to smooth the data processed by the first-order derivative, so as to reduce the redundant zero-crossing characteristics caused by factors such as noise and interference. Let the data after the first derivative process be

, which contains active ingredients

and random error components caused by random noise interference, etc.

,which is:

，

为数据长度。根据滑动平均原理，在

个非平稳数据中，每

个相邻数据的区间内点接近平稳，因此，将

个数据逐个滑动的取

个相邻数据做平均来表示平滑数据，则一次矩形平滑可表示为：in,

,

is the data length. According to the moving average principle, in

In non-stationary data, each

The points in the interval of adjacent data are close to stationary, therefore, the

The data is fetched by sliding one by one

Averaging adjacent data to represent smooth data, a rectangular smoothing can be expressed as:

Further, pseudo-Gaussian smoothing can be expressed as:

。通过pseudo-Gaussian平滑处理后，可滤除掉数据中频繁的随机起伏，显示平滑的变化趋势。in,

. After pseudo-Gaussian smoothing, the frequent random fluctuations in the data can be filtered out and a smooth change trend can be displayed.

Zero-crossing detection refers to the method used to detect the corresponding position when the function sign changes. It includes down zero crossing detection and up zero crossing detection. The positions corresponding to the peaks and troughs of the signal data can be obtained respectively through the downward zero-crossing detection and the upward zero-crossing detection. Since the spectral peak of the signal is mainly considered in this technical solution, after pseudo-Gaussian smoothing is performed on the signal data, the positive spectral peak position of the signal can be located by using the downward zero-crossing detection method.

Please refer to FIG. 4 , which is a schematic diagram of positive peak positioning provided by an embodiment of the present application. As shown in Figure 4, the spectral data can be represented by the amplitude spectrum, as shown in the gray area at the top in Figure 4, and the data processed by the first derivative is shown in the white area in the middle of Figure 4. Pseudo the data processed by the first derivative. -Gaussian smoothing, which can filter out the frequent random fluctuations in the data, and show a smooth change trend. The smoothed data is shown in the gray line in Figure 4, and the circle is the peak location point.

。如此，可以根据频率计算待处理数据的驻留时间。Through the positive peak positioning mechanism, all positive spectral peaks in the signal spectrum can be searched to obtain the frequency corresponding to each positive spectral peak

. In this way, the dwell time of the data to be processed can be calculated according to the frequency.

In an optional embodiment, calculating the dwell time of the data to be processed according to the frequency can be implemented in the following ways:

Calculates frequency steps for positive spectral peaks based on frequency. Calculate the frequency interval of each positive spectral peak according to the frequency step, and take the smallest frequency interval as the frequency step of the data to be processed. Calculate the frequency difference of all adjacent frequencies, and filter out the frequency set whose frequency difference is greater than half of the frequency step size. Obtains the dwell time of the data to be processed based on the frequency set.

，并根据频率间隔可得待处理数据的频率步长

，即：For example, first calculate the frequency spacing of the spectral peaks

, and the frequency step size of the data to be processed can be obtained according to the frequency interval

,which is:

、频率时间分析处理结果和功率时间分析处理结果得到相邻频率差大于

的频率集

，则待处理数据的驻留时间

可表示为：Combine frequency steps

, the frequency-time analysis and processing results and the power-time analysis and processing results show that the adjacent frequency difference is greater than

frequency set

, the residence time of the data to be processed

can be expressed as:

。in,

.

In this way, the key parameter frequency hopping rate of the data to be processed can be obtained according to the dwell time.

可以表示为：Since the frequency hopping rate of the data to be processed is equal to the inverse of the dwell time, the frequency hopping rate

It can be expressed as:

In an optional embodiment, the method further includes:

According to the analysis result, it is determined whether the data to be processed includes multiple complete switching time periods and includes multiple complete dwell time periods.

If it is determined that the data to be processed includes multiple complete switching time periods and multiple complete dwell time periods, the dwell time corresponding to the smallest dwell time period is taken as the dwell time of the data to be processed, and the number of dwell times is calculated. The average value of the switching time corresponding to a complete switching time period, and the average value is used as the switching time of the data to be processed.

If it is determined that the data to be processed only includes a complete switching period, the switching time and dwell time of the switching period are calculated.

If it is determined that the data to be processed does not contain any complete switching time period, a prompt message indicating that one hop is not completed is output, and the dwell time of the data to be processed is calculated.

It can be understood that, in the above three cases, the principles of calculating the dwell time and the switching time of the data to be processed can be referred to the above process and method for calculating the dwell time and the switching time, and will not be repeated here.

Further, please refer to FIG. 5 and FIG. 6 in combination, FIG. 5 is a schematic diagram of comparison of handover time estimation errors provided by an embodiment of the present application, and FIG. 6 is a schematic diagram of comparison of residence time estimation errors provided by an embodiment of the present application.

来衡量，计算方式为：In this embodiment of the present application, the estimated performance of the switching time and the dwell time of the data to be processed can be determined by the relative error

To measure, the calculation method is:

The experimental environment of the comparative analysis can use the SMBV100A vector signal generator of Germany Rohde & Schwarz (ROHDE & SCHWARZ) to generate the signal according to the parameter settings, and collect the signal on the real-time spectrum analyzer of the American Tektronix RSA6114A model. Data processing. The simulation experiment is to test the collected 50 groups of data to be processed under MATLAB. The performance comparison is shown in Figure 5 and Figure 6.

Through simulation comparison, it can be seen from Figure 5 that for the estimation of the switching time of the data to be processed, the estimation error based on the frequency-time analysis and processing mechanism is larger than the estimation error based on the power-time analysis and processing mechanism. The frequency-time analysis processing mechanism can estimate more accurate switching time. It can be seen from Figure 6 that for the estimation of the residence time of the data to be processed, the estimation performance based on the power-time analysis and processing mechanism is poor, and there is a case of double estimation, and the power-time analysis and processing combined with the frequency-time analysis and processing mechanism can avoid this problem. , and compared with the processing mechanism based on frequency-time analysis, a smaller estimation error can be obtained, ensuring effective estimation of the parameters of the switching time and dwell time of the data to be processed.

Based on the same inventive concept, please refer to FIG. 7 , which is a block diagram of functional modules of an apparatus for estimating parameters of a frequency hopping signal provided by an embodiment of the present application. The embodiment of the present application also provides a frequency hopping signal parameter estimation apparatus 130 corresponding to the frequency hopping signal parameter estimation method shown in FIG. 2 , and the apparatus includes:

The obtaining module 131 is used to obtain the data to be processed.

The analysis processing module 132 is configured to perform power time analysis processing and frequency time analysis processing on the data to be processed to obtain analysis results.

The estimation module 133 is configured to judge whether the data to be processed is a frequency hopping signal according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, estimate the parameters of the data to be processed according to the analysis result to obtain an estimation result.

Since the principle of the device in the embodiment of the present application for solving the problem is similar to the principle of the above-mentioned method for estimating the parameters of the frequency hopping signal in the embodiment of the present application, the implementation principle of the device may refer to the implementation principle of the method, and the repetition will not be repeated.

Embodiments of the present application also provide a readable storage medium, where a computer program is stored in the readable storage medium, and when the computer program is executed, the above-mentioned method, apparatus, electronic device, and readable storage medium for estimating a frequency hopping signal parameter are implemented.

To sum up, the embodiments of the present application provide a method, device, electronic device, and readable storage medium for estimating parameters of a frequency hopping signal. Get the analysis results. Whether the data to be processed is a frequency hopping signal is determined according to the analysis result, and if it is determined that the data to be processed is a frequency hopping signal, the parameters of the data to be processed are estimated according to the analysis result to obtain an estimation result. In this way, the data to be processed is analyzed and processed in combination with the power time analysis process and the frequency time analysis process, which improves the accuracy of frequency hopping parameter estimation in complex engineering applications.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, All should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (9)

1. A method for estimating parameters of a frequency hopping signal, the method comprising:

acquiring data to be processed;

performing power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;

judging whether the data to be processed is a frequency hopping signal or not according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result;

the frequency-time analysis processing comprises short-time Fourier transform processing, spectrogram processing and frequency clustering processing, the power-time analysis processing and the frequency-time analysis processing are carried out on the data to be processed, and the step of obtaining the analysis result comprises the following steps:

performing power time analysis processing on the data to be processed to obtain a power time analysis result;

performing power time analysis processing on the data to be processed according to the following formula:

；

wherein,

the results of the power-time analysis are shown,

representing the data to be processed;

carrying out short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;

performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;

performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;

and taking the power time analysis result and the window frequency data of the data to be processed as analysis results.

2. The method according to claim 1, wherein the data to be processed includes an in-phase quadrature signal and a sampling frequency, the spectrogram processing result includes spectrum data, and the step of estimating the parameter of the data to be processed according to the analysis result to obtain the estimation result includes:

setting a time multiple threshold value and a power judgment threshold value according to the in-phase orthogonal signal and the sampling frequency included in the data to be processed;

calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold;

performing positive peak positioning processing on the frequency spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed;

and taking the switching time and the residence time as estimation results.

3. The method according to claim 2, wherein the step of calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold comprises:

obtaining a switching time period of which the power is smaller than the power judgment threshold in the power time analysis result;

obtaining a power conversion critical point according to the time multiple threshold, and calculating a starting point and an end point of each switching time period according to the power conversion critical point;

calculating initial switching time of a starting point and an end point of each switching time period, and calculating an average value of all the initial switching time;

and taking the average value of the initial switching time as the switching time of the data to be processed.

4. The method according to claim 2, wherein the positive peak location processing includes first derivative processing, smoothing processing, and zero crossing point detection, and the step of performing positive peak location processing on the spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed includes:

calculating a first derivative of the spectrum data to obtain a derivative result, wherein the derivative result represents all maximum value points of the spectrum data;

smoothing the derivation result to obtain a smoothed derivation result;

carrying out zero crossing point detection on the derivative result after the smoothing treatment to obtain a signal spectrum peak;

screening to obtain a positive spectrum peak in the signal spectrum peaks and a frequency corresponding to the positive spectrum peak;

and calculating the residence time of the data to be processed according to the frequency.

5. The method according to claim 4, wherein the step of calculating the dwell time of the data to be processed according to the frequency comprises:

calculating the frequency step of the positive spectrum peak according to the frequency;

calculating the frequency interval of each positive spectral peak according to the frequency step, and taking the minimum frequency interval as the frequency step of the data to be processed;

calculating the frequency difference values of all adjacent frequencies, and screening to obtain a frequency set with the frequency difference value larger than half of the frequency step;

and obtaining the residence time of the data to be processed according to the frequency set.

6. The method of claim 1, further comprising:

judging whether the data to be processed comprises a plurality of complete switching time periods and a plurality of complete residence time periods according to the analysis result;

if it is determined that the to-be-processed data includes a plurality of complete switching time periods and a plurality of complete residence time periods, the residence time corresponding to the minimum residence time period is used as the residence time of the to-be-processed data, the average value of the switching times corresponding to the complete switching time periods is calculated, and the average value is used as the switching time of the to-be-processed data.

7. An apparatus for estimating parameters of a frequency hopping signal, the apparatus comprising:

the acquisition module is used for acquiring data to be processed;

the analysis processing module is used for carrying out power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;

the estimation module is used for judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result;

the frequency-time analysis processing comprises short-time Fourier transform processing, spectrogram processing and frequency clustering processing, the power-time analysis processing and the frequency-time analysis processing are carried out on the data to be processed, and the analysis result is obtained by the following steps:

performing power time analysis processing on the data to be processed to obtain a power time analysis result;

performing power time analysis processing on the data to be processed according to the following formula:

；

wherein,

the results of the power-time analysis are shown,

representing the data to be processed;

carrying out short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;

performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;

performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;

and taking the power time analysis result and the window frequency data of the data to be processed as analysis results.

8. An electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for estimating parameters of a frequency hopping signal according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that the readable storage medium stores a computer program which, when executed, implements the steps of the frequency hopping signal parameter estimation method of any one of claims 1 to 6.

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